


from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings
from langchain_deepseek import ChatDeepSeek

from langchain_community.embeddings.zhipuai import ZhipuAIEmbeddings


embed = ZhipuAIEmbeddings(
    model="embedding-2",
    api_key="f387f5e4837d4e4bba6d267682a957c9.PmPiTw8qVlsI2Oi5",
)


vector_store = InMemoryVectorStore(embed)


from langchain_core.documents import Document

document_1 = Document(id="1", page_content="foo", metadata={"baz": "bar"})
document_2 = Document(id="2", page_content="thud", metadata={"bar": "baz"})
document_3 = Document(id="3", page_content="i will be deleted :(")

documents = [document_1, document_2, document_3]
vector_store.add_documents(documents=documents)


print("OK")